Overview

Dataset statistics

Number of variables19
Number of observations42308
Missing cells1737
Missing cells (%)0.2%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory6.5 MiB
Average record size in memory160.0 B

Variable types

Numeric7
Categorical4
DateTime2
Text6

Alerts

LowDoc is highly imbalanced (64.0%)Imbalance
RevLineCr has 1041 (2.5%) missing valuesMissing
LowDoc has 520 (1.2%) missing valuesMissing
NoEmp has 3048 (7.2%) zerosZeros
CreateJob has 28870 (68.2%) zerosZeros
RetainedJob has 25988 (61.4%) zerosZeros
FranchiseCode has 26458 (62.5%) zerosZeros
Sector has 9821 (23.2%) zerosZeros

Reproduction

Analysis started2024-01-27 16:07:42.745896
Analysis finished2024-01-27 16:08:02.451343
Duration19.71 seconds
Software versionydata-profiling vv4.6.4
Download configurationconfig.json

Variables

Term
Real number (ℝ)

Distinct229
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean108.51522
Minimum0
Maximum360
Zeros95
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size661.1 KiB
2024-01-27T16:08:02.588999image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile12
Q156
median82
Q3168
95-th percentile293
Maximum360
Range360
Interquartile range (IQR)112

Descriptive statistics

Standard deviation84.894703
Coefficient of variation (CV)0.78232991
Kurtosis-0.27868668
Mean108.51522
Median Absolute Deviation (MAD)30
Skewness1.0274118
Sum4591062
Variance7207.1106
MonotonicityNot monotonic
2024-01-27T16:08:02.854769image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
82 3502
 
8.3%
83 2957
 
7.0%
84 1497
 
3.5%
57 1359
 
3.2%
81 1343
 
3.2%
58 1291
 
3.1%
59 1090
 
2.6%
56 1047
 
2.5%
240 887
 
2.1%
60 857
 
2.0%
Other values (219) 26478
62.6%
ValueCountFrequency (%)
0 95
0.2%
1 66
 
0.2%
2 65
 
0.2%
3 94
0.2%
4 110
0.3%
5 140
0.3%
6 130
0.3%
7 168
0.4%
8 167
0.4%
9 234
0.6%
ValueCountFrequency (%)
360 2
 
< 0.1%
325 8
 
< 0.1%
312 6
 
< 0.1%
311 20
 
< 0.1%
310 27
 
0.1%
309 50
 
0.1%
308 70
0.2%
306 91
0.2%
303 137
0.3%
302 142
0.3%

NoEmp
Real number (ℝ)

ZEROS 

Distinct190
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.6947149
Minimum0
Maximum208
Zeros3048
Zeros (%)7.2%
Negative0
Negative (%)0.0%
Memory size661.1 KiB
2024-01-27T16:08:03.092914image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median4
Q312
95-th percentile40
Maximum208
Range208
Interquartile range (IQR)10

Descriptive statistics

Standard deviation17.242651
Coefficient of variation (CV)1.778562
Kurtosis40.904384
Mean9.6947149
Median Absolute Deviation (MAD)3
Skewness5.455337
Sum410164
Variance297.30903
MonotonicityNot monotonic
2024-01-27T16:08:03.312068image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3 5608
13.3%
2 5354
12.7%
4 4791
11.3%
1 4526
 
10.7%
5 3156
 
7.5%
0 3048
 
7.2%
6 1760
 
4.2%
16 917
 
2.2%
7 891
 
2.1%
17 885
 
2.1%
Other values (180) 11372
26.9%
ValueCountFrequency (%)
0 3048
7.2%
1 4526
10.7%
2 5354
12.7%
3 5608
13.3%
4 4791
11.3%
5 3156
7.5%
6 1760
 
4.2%
7 891
 
2.1%
8 560
 
1.3%
9 515
 
1.2%
ValueCountFrequency (%)
208 1
 
< 0.1%
201 1
 
< 0.1%
199 2
< 0.1%
196 1
 
< 0.1%
195 1
 
< 0.1%
194 1
 
< 0.1%
193 2
< 0.1%
192 2
< 0.1%
190 1
 
< 0.1%
189 3
< 0.1%

NewExist
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size661.1 KiB
1.0
33472 
2.0
8836 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters126924
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row2.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 33472
79.1%
2.0 8836
 
20.9%

Length

2024-01-27T16:08:03.497193image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-27T16:08:03.671363image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
1.0 33472
79.1%
2.0 8836
 
20.9%

Most occurring characters

ValueCountFrequency (%)
. 42308
33.3%
0 42308
33.3%
1 33472
26.4%
2 8836
 
7.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 84616
66.7%
Other Punctuation 42308
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 42308
50.0%
1 33472
39.6%
2 8836
 
10.4%
Other Punctuation
ValueCountFrequency (%)
. 42308
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 126924
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 42308
33.3%
0 42308
33.3%
1 33472
26.4%
2 8836
 
7.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 126924
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 42308
33.3%
0 42308
33.3%
1 33472
26.4%
2 8836
 
7.0%

CreateJob
Real number (ℝ)

ZEROS 

Distinct49
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.1865605
Minimum0
Maximum86
Zeros28870
Zeros (%)68.2%
Negative0
Negative (%)0.0%
Memory size661.1 KiB
2024-01-27T16:08:03.863318image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q32
95-th percentile13
Maximum86
Range86
Interquartile range (IQR)2

Descriptive statistics

Standard deviation5.1316801
Coefficient of variation (CV)2.3469189
Kurtosis27.637784
Mean2.1865605
Median Absolute Deviation (MAD)0
Skewness4.2278179
Sum92509
Variance26.33414
MonotonicityNot monotonic
2024-01-27T16:08:04.098066image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
0 28870
68.2%
3 3327
 
7.9%
1 2457
 
5.8%
4 1443
 
3.4%
8 677
 
1.6%
9 601
 
1.4%
7 570
 
1.3%
10 518
 
1.2%
2 491
 
1.2%
11 389
 
0.9%
Other values (39) 2965
 
7.0%
ValueCountFrequency (%)
0 28870
68.2%
1 2457
 
5.8%
2 491
 
1.2%
3 3327
 
7.9%
4 1443
 
3.4%
5 88
 
0.2%
6 228
 
0.5%
7 570
 
1.3%
8 677
 
1.6%
9 601
 
1.4%
ValueCountFrequency (%)
86 2
 
< 0.1%
75 1
 
< 0.1%
70 1
 
< 0.1%
60 3
 
< 0.1%
57 3
 
< 0.1%
56 7
 
< 0.1%
50 9
< 0.1%
48 14
< 0.1%
47 12
< 0.1%
46 18
< 0.1%

RetainedJob
Real number (ℝ)

ZEROS 

Distinct85
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.4996455
Minimum0
Maximum175
Zeros25988
Zeros (%)61.4%
Negative0
Negative (%)0.0%
Memory size661.1 KiB
2024-01-27T16:08:04.425255image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q34
95-th percentile15
Maximum175
Range175
Interquartile range (IQR)4

Descriptive statistics

Standard deviation8.1344985
Coefficient of variation (CV)2.3243779
Kurtosis49.299653
Mean3.4996455
Median Absolute Deviation (MAD)0
Skewness5.4665105
Sum148063
Variance66.170066
MonotonicityNot monotonic
2024-01-27T16:08:04.665813image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 25988
61.4%
1 3822
 
9.0%
8 1223
 
2.9%
3 1071
 
2.5%
9 1063
 
2.5%
7 1017
 
2.4%
10 821
 
1.9%
11 791
 
1.9%
4 771
 
1.8%
2 768
 
1.8%
Other values (75) 4973
 
11.8%
ValueCountFrequency (%)
0 25988
61.4%
1 3822
 
9.0%
2 768
 
1.8%
3 1071
 
2.5%
4 771
 
1.8%
5 317
 
0.7%
6 532
 
1.3%
7 1017
 
2.4%
8 1223
 
2.9%
9 1063
 
2.5%
ValueCountFrequency (%)
175 2
 
< 0.1%
136 1
 
< 0.1%
130 1
 
< 0.1%
125 4
< 0.1%
118 1
 
< 0.1%
110 3
< 0.1%
102 1
 
< 0.1%
100 5
< 0.1%
95 2
 
< 0.1%
91 2
 
< 0.1%

FranchiseCode
Real number (ℝ)

ZEROS 

Distinct271
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2025.0608
Minimum0
Maximum90709
Zeros26458
Zeros (%)62.5%
Negative0
Negative (%)0.0%
Memory size661.1 KiB
2024-01-27T16:08:04.902143image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile1
Maximum90709
Range90709
Interquartile range (IQR)1

Descriptive statistics

Standard deviation10633.754
Coefficient of variation (CV)5.2510787
Kurtosis34.228771
Mean2025.0608
Median Absolute Deviation (MAD)0
Skewness5.7895406
Sum85676274
Variance1.1307672 × 108
MonotonicityNot monotonic
2024-01-27T16:08:05.117519image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 26458
62.5%
1 13878
32.8%
960 174
 
0.4%
24902 18
 
< 0.1%
29887 17
 
< 0.1%
28555 15
 
< 0.1%
35320 15
 
< 0.1%
30229 15
 
< 0.1%
27260 14
 
< 0.1%
34850 14
 
< 0.1%
Other values (261) 1690
 
4.0%
ValueCountFrequency (%)
0 26458
62.5%
1 13878
32.8%
960 174
 
0.4%
10461 1
 
< 0.1%
10481 1
 
< 0.1%
10484 4
 
< 0.1%
10528 1
 
< 0.1%
10580 1
 
< 0.1%
10621 3
 
< 0.1%
10676 2
 
< 0.1%
ValueCountFrequency (%)
90709 1
 
< 0.1%
89785 2
< 0.1%
89769 2
< 0.1%
89655 1
 
< 0.1%
89640 2
< 0.1%
89352 4
< 0.1%
89350 2
< 0.1%
88875 1
 
< 0.1%
87350 1
 
< 0.1%
86720 4
< 0.1%

RevLineCr
Categorical

MISSING 

Distinct4
Distinct (%)< 0.1%
Missing1041
Missing (%)2.5%
Memory size661.1 KiB
N
27829 
Y
7454 
0
5346 
T
 
638

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters41267
Distinct characters4
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowT
2nd rowY
3rd rowN
4th rowY
5th rowN

Common Values

ValueCountFrequency (%)
N 27829
65.8%
Y 7454
 
17.6%
0 5346
 
12.6%
T 638
 
1.5%
(Missing) 1041
 
2.5%

Length

2024-01-27T16:08:05.307804image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-27T16:08:05.472957image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
n 27829
67.4%
y 7454
 
18.1%
0 5346
 
13.0%
t 638
 
1.5%

Most occurring characters

ValueCountFrequency (%)
N 27829
67.4%
Y 7454
 
18.1%
0 5346
 
13.0%
T 638
 
1.5%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 35921
87.0%
Decimal Number 5346
 
13.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
N 27829
77.5%
Y 7454
 
20.8%
T 638
 
1.8%
Decimal Number
ValueCountFrequency (%)
0 5346
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 35921
87.0%
Common 5346
 
13.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
N 27829
77.5%
Y 7454
 
20.8%
T 638
 
1.8%
Common
ValueCountFrequency (%)
0 5346
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 41267
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
N 27829
67.4%
Y 7454
 
18.1%
0 5346
 
13.0%
T 638
 
1.5%

LowDoc
Categorical

IMBALANCE  MISSING 

Distinct6
Distinct (%)< 0.1%
Missing520
Missing (%)1.2%
Memory size661.1 KiB
N
34360 
Y
5275 
0
 
675
A
 
561
S
 
523

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters41788
Distinct characters6
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowN
2nd rowN
3rd rowN
4th rowN
5th rowY

Common Values

ValueCountFrequency (%)
N 34360
81.2%
Y 5275
 
12.5%
0 675
 
1.6%
A 561
 
1.3%
S 523
 
1.2%
C 394
 
0.9%
(Missing) 520
 
1.2%

Length

2024-01-27T16:08:05.693280image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-27T16:08:05.863972image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
n 34360
82.2%
y 5275
 
12.6%
0 675
 
1.6%
a 561
 
1.3%
s 523
 
1.3%
c 394
 
0.9%

Most occurring characters

ValueCountFrequency (%)
N 34360
82.2%
Y 5275
 
12.6%
0 675
 
1.6%
A 561
 
1.3%
S 523
 
1.3%
C 394
 
0.9%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 41113
98.4%
Decimal Number 675
 
1.6%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
N 34360
83.6%
Y 5275
 
12.8%
A 561
 
1.4%
S 523
 
1.3%
C 394
 
1.0%
Decimal Number
ValueCountFrequency (%)
0 675
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 41113
98.4%
Common 675
 
1.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
N 34360
83.6%
Y 5275
 
12.8%
A 561
 
1.4%
S 523
 
1.3%
C 394
 
1.0%
Common
ValueCountFrequency (%)
0 675
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 41788
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
N 34360
82.2%
Y 5275
 
12.6%
0 675
 
1.6%
A 561
 
1.3%
S 523
 
1.3%
C 394
 
0.9%
Distinct915
Distinct (%)2.2%
Missing160
Missing (%)0.4%
Memory size661.1 KiB
Minimum1977-06-14 00:00:00
Maximum2073-12-06 00:00:00
2024-01-27T16:08:06.066027image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-27T16:08:06.290040image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Sector
Real number (ℝ)

ZEROS 

Distinct24
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean32.966602
Minimum0
Maximum81
Zeros9821
Zeros (%)23.2%
Negative0
Negative (%)0.0%
Memory size661.1 KiB
2024-01-27T16:08:06.583587image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q122
median33
Q344
95-th percentile72
Maximum81
Range81
Interquartile range (IQR)22

Descriptive statistics

Standard deviation22.273527
Coefficient of variation (CV)0.67563915
Kurtosis-0.8931368
Mean32.966602
Median Absolute Deviation (MAD)11
Skewness-0.12484652
Sum1394751
Variance496.11
MonotonicityNot monotonic
2024-01-27T16:08:06.803758image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
0 9821
23.2%
42 7504
17.7%
33 5109
12.1%
44 3963
9.4%
23 3673
 
8.7%
61 2577
 
6.1%
72 2439
 
5.8%
22 1959
 
4.6%
62 1163
 
2.7%
53 906
 
2.1%
Other values (14) 3194
 
7.5%
ValueCountFrequency (%)
0 9821
23.2%
11 8
 
< 0.1%
21 24
 
0.1%
22 1959
 
4.6%
23 3673
 
8.7%
31 155
 
0.4%
32 883
 
2.1%
33 5109
12.1%
42 7504
17.7%
44 3963
9.4%
ValueCountFrequency (%)
81 178
 
0.4%
72 2439
5.8%
71 341
 
0.8%
62 1163
2.7%
61 2577
6.1%
56 606
 
1.4%
55 29
 
0.1%
54 251
 
0.6%
53 906
 
2.1%
52 75
 
0.2%
Distinct3888
Distinct (%)9.2%
Missing0
Missing (%)0.0%
Memory size661.1 KiB
Minimum1977-03-11 00:00:00
Maximum2073-10-17 00:00:00
2024-01-27T16:08:07.018702image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-27T16:08:07.240963image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

ApprovalFY
Real number (ℝ)

Distinct38
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2001.5219
Minimum1974
Maximum2014
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size661.1 KiB
2024-01-27T16:08:07.448731image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum1974
5-th percentile1991
Q11997
median2003
Q32006
95-th percentile2010
Maximum2014
Range40
Interquartile range (IQR)9

Descriptive statistics

Standard deviation5.9015063
Coefficient of variation (CV)0.0029485095
Kurtosis0.14880309
Mean2001.5219
Median Absolute Deviation (MAD)4
Skewness-0.69731103
Sum84680388
Variance34.827776
MonotonicityNot monotonic
2024-01-27T16:08:07.671810image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=38)
ValueCountFrequency (%)
2004 4685
 
11.1%
2007 3807
 
9.0%
2003 3299
 
7.8%
2006 3241
 
7.7%
2005 2744
 
6.5%
1995 2411
 
5.7%
2000 2307
 
5.5%
1996 1791
 
4.2%
2008 1729
 
4.1%
2002 1686
 
4.0%
Other values (28) 14608
34.5%
ValueCountFrequency (%)
1974 3
 
< 0.1%
1977 6
 
< 0.1%
1979 21
 
< 0.1%
1980 54
 
0.1%
1981 11
 
< 0.1%
1982 93
0.2%
1983 67
0.2%
1984 95
0.2%
1985 155
0.4%
1986 68
0.2%
ValueCountFrequency (%)
2014 6
 
< 0.1%
2013 101
 
0.2%
2012 345
 
0.8%
2011 775
 
1.8%
2010 976
 
2.3%
2009 1164
 
2.8%
2008 1729
4.1%
2007 3807
9.0%
2006 3241
7.7%
2005 2744
6.5%

City
Text

Distinct2678
Distinct (%)6.3%
Missing0
Missing (%)0.0%
Memory size661.1 KiB
2024-01-27T16:08:08.209467image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Length

Max length30
Median length27
Mean length8.895528
Min length3

Characters and Unicode

Total characters376352
Distinct characters69
Distinct categories9 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique775 ?
Unique (%)1.8%

Sample

1st rowSUNNYVALE
2nd rowPITTSBURGH
3rd rowLITTLE ROCK
4th rowLITTLE ROCK
5th rowLouisville
ValueCountFrequency (%)
city 1458
 
2.7%
san 1305
 
2.4%
houston 1159
 
2.1%
pittsburgh 911
 
1.7%
lake 770
 
1.4%
salt 672
 
1.2%
nashville 631
 
1.2%
pomona 623
 
1.1%
new 586
 
1.1%
philadelphia 538
 
1.0%
Other values (2336) 45860
84.1%
2024-01-27T16:08:08.795502image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
A 36735
 
9.8%
E 32561
 
8.7%
O 29895
 
7.9%
L 29183
 
7.8%
N 28977
 
7.7%
S 24253
 
6.4%
I 23696
 
6.3%
R 22017
 
5.9%
T 20114
 
5.3%
C 13341
 
3.5%
Other values (59) 115580
30.7%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 353182
93.8%
Space Separator 12210
 
3.2%
Lowercase Letter 10059
 
2.7%
Other Punctuation 383
 
0.1%
Open Punctuation 315
 
0.1%
Close Punctuation 181
 
< 0.1%
Decimal Number 11
 
< 0.1%
Dash Punctuation 10
 
< 0.1%
Modifier Symbol 1
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 36735
 
10.4%
E 32561
 
9.2%
O 29895
 
8.5%
L 29183
 
8.3%
N 28977
 
8.2%
S 24253
 
6.9%
I 23696
 
6.7%
R 22017
 
6.2%
T 20114
 
5.7%
C 13341
 
3.8%
Other values (16) 92410
26.2%
Lowercase Letter
ValueCountFrequency (%)
o 1079
10.7%
e 1048
10.4%
a 1010
10.0%
n 992
9.9%
l 925
9.2%
i 817
8.1%
r 781
7.8%
s 612
 
6.1%
t 523
 
5.2%
u 325
 
3.2%
Other values (16) 1947
19.4%
Decimal Number
ValueCountFrequency (%)
0 3
27.3%
2 2
18.2%
1 2
18.2%
3 1
 
9.1%
6 1
 
9.1%
8 1
 
9.1%
5 1
 
9.1%
Other Punctuation
ValueCountFrequency (%)
. 280
73.1%
' 56
 
14.6%
, 44
 
11.5%
/ 2
 
0.5%
: 1
 
0.3%
Space Separator
ValueCountFrequency (%)
12210
100.0%
Open Punctuation
ValueCountFrequency (%)
( 315
100.0%
Close Punctuation
ValueCountFrequency (%)
) 181
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 10
100.0%
Modifier Symbol
ValueCountFrequency (%)
` 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 363241
96.5%
Common 13111
 
3.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 36735
 
10.1%
E 32561
 
9.0%
O 29895
 
8.2%
L 29183
 
8.0%
N 28977
 
8.0%
S 24253
 
6.7%
I 23696
 
6.5%
R 22017
 
6.1%
T 20114
 
5.5%
C 13341
 
3.7%
Other values (42) 102469
28.2%
Common
ValueCountFrequency (%)
12210
93.1%
( 315
 
2.4%
. 280
 
2.1%
) 181
 
1.4%
' 56
 
0.4%
, 44
 
0.3%
- 10
 
0.1%
0 3
 
< 0.1%
2 2
 
< 0.1%
1 2
 
< 0.1%
Other values (7) 8
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 376352
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 36735
 
9.8%
E 32561
 
8.7%
O 29895
 
7.9%
L 29183
 
7.8%
N 28977
 
7.7%
S 24253
 
6.4%
I 23696
 
6.3%
R 22017
 
5.9%
T 20114
 
5.3%
C 13341
 
3.5%
Other values (59) 115580
30.7%

State
Text

Distinct51
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size661.1 KiB
2024-01-27T16:08:09.022800image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters84616
Distinct characters24
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCA
2nd rowPA
3rd rowAR
4th rowAR
5th rowKY
ValueCountFrequency (%)
ca 6934
 
16.4%
tx 4189
 
9.9%
ny 2880
 
6.8%
pa 2731
 
6.5%
fl 1947
 
4.6%
oh 1221
 
2.9%
ut 1143
 
2.7%
tn 1129
 
2.7%
wa 1095
 
2.6%
ma 1029
 
2.4%
Other values (41) 18010
42.6%
2024-01-27T16:08:09.469340image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
A 14829
17.5%
C 8869
10.5%
N 8459
10.0%
T 7350
 
8.7%
M 5516
 
6.5%
I 4636
 
5.5%
X 4189
 
5.0%
O 4163
 
4.9%
Y 3801
 
4.5%
L 3463
 
4.1%
Other values (14) 19341
22.9%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 84616
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 14829
17.5%
C 8869
10.5%
N 8459
10.0%
T 7350
 
8.7%
M 5516
 
6.5%
I 4636
 
5.5%
X 4189
 
5.0%
O 4163
 
4.9%
Y 3801
 
4.5%
L 3463
 
4.1%
Other values (14) 19341
22.9%

Most occurring scripts

ValueCountFrequency (%)
Latin 84616
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 14829
17.5%
C 8869
10.5%
N 8459
10.0%
T 7350
 
8.7%
M 5516
 
6.5%
I 4636
 
5.5%
X 4189
 
5.0%
O 4163
 
4.9%
Y 3801
 
4.5%
L 3463
 
4.1%
Other values (14) 19341
22.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 84616
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 14829
17.5%
C 8869
10.5%
N 8459
10.0%
T 7350
 
8.7%
M 5516
 
6.5%
I 4636
 
5.5%
X 4189
 
5.0%
O 4163
 
4.9%
Y 3801
 
4.5%
L 3463
 
4.1%
Other values (14) 19341
22.9%
Distinct52
Distinct (%)0.1%
Missing16
Missing (%)< 0.1%
Memory size661.1 KiB
2024-01-27T16:08:09.688882image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters84584
Distinct characters24
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowCA
2nd rowPA
3rd rowAR
4th rowAR
5th rowDE
ValueCountFrequency (%)
ca 6491
15.3%
nc 3266
 
7.7%
il 2947
 
7.0%
oh 2878
 
6.8%
ri 2586
 
6.1%
tx 2525
 
6.0%
sd 2285
 
5.4%
ny 2159
 
5.1%
pa 1227
 
2.9%
ut 1119
 
2.6%
Other values (42) 14809
35.0%
2024-01-27T16:08:10.074705image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
A 11324
13.4%
C 10833
12.8%
N 8768
10.4%
I 7456
 
8.8%
O 5266
 
6.2%
T 5156
 
6.1%
L 4251
 
5.0%
M 3987
 
4.7%
D 3756
 
4.4%
H 3339
 
3.9%
Other values (14) 20448
24.2%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 84584
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 11324
13.4%
C 10833
12.8%
N 8768
10.4%
I 7456
 
8.8%
O 5266
 
6.2%
T 5156
 
6.1%
L 4251
 
5.0%
M 3987
 
4.7%
D 3756
 
4.4%
H 3339
 
3.9%
Other values (14) 20448
24.2%

Most occurring scripts

ValueCountFrequency (%)
Latin 84584
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 11324
13.4%
C 10833
12.8%
N 8768
10.4%
I 7456
 
8.8%
O 5266
 
6.2%
T 5156
 
6.1%
L 4251
 
5.0%
M 3987
 
4.7%
D 3756
 
4.4%
H 3339
 
3.9%
Other values (14) 20448
24.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 84584
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 11324
13.4%
C 10833
12.8%
N 8768
10.4%
I 7456
 
8.8%
O 5266
 
6.2%
T 5156
 
6.1%
L 4251
 
5.0%
M 3987
 
4.7%
D 3756
 
4.4%
H 3339
 
3.9%
Other values (14) 20448
24.2%
Distinct2659
Distinct (%)6.3%
Missing0
Missing (%)0.0%
Memory size661.1 KiB
2024-01-27T16:08:10.373720image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Length

Max length14
Median length12
Mean length11.491018
Min length10

Characters and Unicode

Total characters486162
Distinct characters14
Distinct categories4 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique615 ?
Unique (%)1.5%

Sample

1st row$25,000.00
2nd row$15,000.00
3rd row$28,000.00
4th row$7,500.00
5th row$91,000.00
ValueCountFrequency (%)
100,000.00 2671
 
6.3%
50,000.00 2168
 
5.1%
25,000.00 1425
 
3.4%
5,000.00 1230
 
2.9%
60,000.00 1072
 
2.5%
150,000.00 935
 
2.2%
80,000.00 918
 
2.2%
145,000.00 791
 
1.9%
10,000.00 753
 
1.8%
17,000.00 720
 
1.7%
Other values (2649) 29625
70.0%
2024-01-27T16:08:10.938025image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 216896
44.6%
, 43211
 
8.9%
$ 42308
 
8.7%
. 42308
 
8.7%
42308
 
8.7%
5 20915
 
4.3%
1 18498
 
3.8%
2 13195
 
2.7%
3 9657
 
2.0%
4 9032
 
1.9%
Other values (4) 27834
 
5.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 316027
65.0%
Other Punctuation 85519
 
17.6%
Currency Symbol 42308
 
8.7%
Space Separator 42308
 
8.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 216896
68.6%
5 20915
 
6.6%
1 18498
 
5.9%
2 13195
 
4.2%
3 9657
 
3.1%
4 9032
 
2.9%
7 7882
 
2.5%
6 7591
 
2.4%
8 6554
 
2.1%
9 5807
 
1.8%
Other Punctuation
ValueCountFrequency (%)
, 43211
50.5%
. 42308
49.5%
Currency Symbol
ValueCountFrequency (%)
$ 42308
100.0%
Space Separator
ValueCountFrequency (%)
42308
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 486162
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 216896
44.6%
, 43211
 
8.9%
$ 42308
 
8.7%
. 42308
 
8.7%
42308
 
8.7%
5 20915
 
4.3%
1 18498
 
3.8%
2 13195
 
2.7%
3 9657
 
2.0%
4 9032
 
1.9%
Other values (4) 27834
 
5.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 486162
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 216896
44.6%
, 43211
 
8.9%
$ 42308
 
8.7%
. 42308
 
8.7%
42308
 
8.7%
5 20915
 
4.3%
1 18498
 
3.8%
2 13195
 
2.7%
3 9657
 
2.0%
4 9032
 
1.9%
Other values (4) 27834
 
5.7%

GrAppv
Text

Distinct1410
Distinct (%)3.3%
Missing0
Missing (%)0.0%
Memory size661.1 KiB
2024-01-27T16:08:11.256018image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Length

Max length14
Median length12
Mean length11.467146
Min length10

Characters and Unicode

Total characters485152
Distinct characters14
Distinct categories4 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique251 ?
Unique (%)0.6%

Sample

1st row$25,000.00
2nd row$15,000.00
3rd row$28,000.00
4th row$7,500.00
5th row$93,000.00
ValueCountFrequency (%)
100,000.00 3158
 
7.5%
50,000.00 3077
 
7.3%
25,000.00 2145
 
5.1%
10,000.00 1376
 
3.3%
5,000.00 1365
 
3.2%
60,000.00 1127
 
2.7%
150,000.00 1027
 
2.4%
80,000.00 940
 
2.2%
15,000.00 882
 
2.1%
30,000.00 866
 
2.0%
Other values (1400) 26345
62.3%
2024-01-27T16:08:11.805856image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 233805
48.2%
, 43226
 
8.9%
$ 42308
 
8.7%
. 42308
 
8.7%
42308
 
8.7%
5 20670
 
4.3%
1 16062
 
3.3%
2 11835
 
2.4%
3 7344
 
1.5%
4 6509
 
1.3%
Other values (4) 18777
 
3.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 315002
64.9%
Other Punctuation 85534
 
17.6%
Currency Symbol 42308
 
8.7%
Space Separator 42308
 
8.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 233805
74.2%
5 20670
 
6.6%
1 16062
 
5.1%
2 11835
 
3.8%
3 7344
 
2.3%
4 6509
 
2.1%
7 5654
 
1.8%
6 5505
 
1.7%
8 4472
 
1.4%
9 3146
 
1.0%
Other Punctuation
ValueCountFrequency (%)
, 43226
50.5%
. 42308
49.5%
Currency Symbol
ValueCountFrequency (%)
$ 42308
100.0%
Space Separator
ValueCountFrequency (%)
42308
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 485152
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 233805
48.2%
, 43226
 
8.9%
$ 42308
 
8.7%
. 42308
 
8.7%
42308
 
8.7%
5 20670
 
4.3%
1 16062
 
3.3%
2 11835
 
2.4%
3 7344
 
1.5%
4 6509
 
1.3%
Other values (4) 18777
 
3.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 485152
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 233805
48.2%
, 43226
 
8.9%
$ 42308
 
8.7%
. 42308
 
8.7%
42308
 
8.7%
5 20670
 
4.3%
1 16062
 
3.3%
2 11835
 
2.4%
3 7344
 
1.5%
4 6509
 
1.3%
Other values (4) 18777
 
3.9%
Distinct1992
Distinct (%)4.7%
Missing0
Missing (%)0.0%
Memory size661.1 KiB
2024-01-27T16:08:12.171284image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Length

Max length14
Median length11
Mean length11.283776
Min length10

Characters and Unicode

Total characters477394
Distinct characters14
Distinct categories4 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique416 ?
Unique (%)1.0%

Sample

1st row$21,250.00
2nd row$7,500.00
3rd row$23,800.00
4th row$6,375.00
5th row$93,000.00
ValueCountFrequency (%)
25,000.00 2438
 
5.8%
12,500.00 1688
 
4.0%
90,000.00 998
 
2.4%
5,000.00 997
 
2.4%
50,000.00 964
 
2.3%
4,250.00 920
 
2.2%
116,000.00 777
 
1.8%
51,000.00 775
 
1.8%
80,000.00 706
 
1.7%
13,600.00 704
 
1.7%
Other values (1982) 31341
74.1%
2024-01-27T16:08:12.753615image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 192785
40.4%
, 42743
 
9.0%
$ 42308
 
8.9%
. 42308
 
8.9%
42308
 
8.9%
5 28618
 
6.0%
2 18888
 
4.0%
1 18451
 
3.9%
3 9781
 
2.0%
7 9704
 
2.0%
Other values (4) 29500
 
6.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 307727
64.5%
Other Punctuation 85051
 
17.8%
Currency Symbol 42308
 
8.9%
Space Separator 42308
 
8.9%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 192785
62.6%
5 28618
 
9.3%
2 18888
 
6.1%
1 18451
 
6.0%
3 9781
 
3.2%
7 9704
 
3.2%
6 8122
 
2.6%
4 7716
 
2.5%
8 7044
 
2.3%
9 6618
 
2.2%
Other Punctuation
ValueCountFrequency (%)
, 42743
50.3%
. 42308
49.7%
Currency Symbol
ValueCountFrequency (%)
$ 42308
100.0%
Space Separator
ValueCountFrequency (%)
42308
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 477394
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 192785
40.4%
, 42743
 
9.0%
$ 42308
 
8.9%
. 42308
 
8.9%
42308
 
8.9%
5 28618
 
6.0%
2 18888
 
4.0%
1 18451
 
3.9%
3 9781
 
2.0%
7 9704
 
2.0%
Other values (4) 29500
 
6.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 477394
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 192785
40.4%
, 42743
 
9.0%
$ 42308
 
8.9%
. 42308
 
8.9%
42308
 
8.9%
5 28618
 
6.0%
2 18888
 
4.0%
1 18451
 
3.9%
3 9781
 
2.0%
7 9704
 
2.0%
Other values (4) 29500
 
6.2%

UrbanRural
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size661.1 KiB
0
24344 
1
11508 
2
6456 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters42308
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row1
4th row2
5th row0

Common Values

ValueCountFrequency (%)
0 24344
57.5%
1 11508
27.2%
2 6456
 
15.3%

Length

2024-01-27T16:08:13.050325image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-27T16:08:13.254408image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
0 24344
57.5%
1 11508
27.2%
2 6456
 
15.3%

Most occurring characters

ValueCountFrequency (%)
0 24344
57.5%
1 11508
27.2%
2 6456
 
15.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 42308
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 24344
57.5%
1 11508
27.2%
2 6456
 
15.3%

Most occurring scripts

ValueCountFrequency (%)
Common 42308
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 24344
57.5%
1 11508
27.2%
2 6456
 
15.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 42308
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 24344
57.5%
1 11508
27.2%
2 6456
 
15.3%

Interactions

2024-01-27T16:07:59.896904image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-27T16:07:52.630146image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-27T16:07:53.985829image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-27T16:07:55.064210image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-27T16:07:56.230165image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-27T16:07:57.503560image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-27T16:07:58.661950image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-27T16:08:00.071181image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-27T16:07:52.765421image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-27T16:07:54.143375image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-27T16:07:55.228270image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-27T16:07:56.438244image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-27T16:07:57.719623image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-27T16:07:58.829785image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-27T16:08:00.224144image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-27T16:07:53.066512image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-27T16:07:54.279903image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-27T16:07:55.393221image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-27T16:07:56.610741image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-27T16:07:57.871098image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-27T16:07:59.007740image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-27T16:08:00.378173image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-27T16:07:53.262303image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-27T16:07:54.424584image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-27T16:07:55.587258image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-27T16:07:56.762520image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-27T16:07:58.008891image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-27T16:07:59.178375image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-27T16:08:00.583484image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-27T16:07:53.451729image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-27T16:07:54.585267image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-27T16:07:55.750281image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-27T16:07:56.931581image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-27T16:07:58.176244image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-27T16:07:59.342982image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-27T16:08:00.745190image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-27T16:07:53.597409image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-27T16:07:54.728694image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-27T16:07:55.894212image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-27T16:07:57.084125image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-27T16:07:58.320229image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-27T16:07:59.499449image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-27T16:08:00.928733image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-27T16:07:53.767551image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-27T16:07:54.894283image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-27T16:07:56.060238image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-27T16:07:57.292580image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-27T16:07:58.486284image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-27T16:07:59.674676image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Missing values

2024-01-27T16:08:01.388286image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-01-27T16:08:01.954060image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

TermNoEmpNewExistCreateJobRetainedJobFranchiseCodeRevLineCrLowDocDisbursementDateSectorApprovalDateApprovalFYCityStateBankStateDisbursementGrossGrAppvSBA_AppvUrbanRural
42307521.0100TN31-Jul-04236-Aug-072007SUNNYVALECACA$25,000.00$25,000.00$21,250.001
42308235131.091477725YN11-Jan-95448-Mar-042004PITTSBURGHPAPA$15,000.00$15,000.00$7,500.000
423093152.0000NNaNNaN5627-Feb-072007LITTLE ROCKARAR$28,000.00$28,000.00$23,800.001
4231012041.0010YN30-Apr-996219-Dec-971998LITTLE ROCKARAR$7,500.00$7,500.00$6,375.002
4231163131.0081NN31-Dec-054210-Jul-092009LouisvilleKYDE$91,000.00$93,000.00$93,000.000
423128321.0178805550Y31-Oct-942330-Nov-941995ORLANDOFLFL$100,000.00$100,000.00$50,000.002
423138131.0001YN5-Apr-11429-Dec-042005CHINOCACA$5,000.00$5,000.00$4,250.001
423143291.0130NN1-Feb-104226-Aug-042004SAN MARCOSTXTX$42,000.00$42,000.00$33,600.001
4231583131.0490NN28-Feb-094213-Dec-052006WEBSTERMAMA$58,547.00$30,000.00$15,000.001
423161502.0000NN30-Nov-03027-Oct-062007FORT WORTHTXTX$50,000.00$50,000.00$40,000.000
TermNoEmpNewExistCreateJobRetainedJobFranchiseCodeRevLineCrLowDocDisbursementDateSectorApprovalDateApprovalFYCityStateBankStateDisbursementGrossGrAppvSBA_AppvUrbanRural
846056051.0021NN5-Jun-928126-Jan-072007INDIANAPOLISINOH$250,000.00$250,000.00$200,000.001
8460617851.0001NN31-Jul-95022-Aug-002000BOISEIDID$100,000.00$100,000.00$80,000.000
84607240161.00000N31-Oct-933313-Apr-891989BUCKEYEAZOR$282,000.00$282,000.00$211,500.001
84608731.014120NN31-May-984421-Dec-901991HUMBOLDTIAIA$152,000.00$152,000.00$152,000.001
8460923721.0000NA31-Jan-957227-Jul-042004HOUSTONTXNC$4,000.00$4,000.00$3,400.000
84610243101.03140NN1-Dec-124223-Apr-122012FT. WRIGHTKYOH$390,000.00$150,000.00$127,500.000
8461117802.0001NN30-Nov-03027-Oct-062007PHILADELPHIAPARI$100,000.00$100,000.00$90,000.000
846124212.0390YN28-Feb-093321-Sep-891989ELMHURSTILIL$17,000.00$17,000.00$13,600.000
8461376151.0000NN31-Jan-0803-Apr-062006NASHVILLETNTN$7,500.00$7,500.00$6,375.000
846143532.01418150YS31-Dec-064413-May-032003EUGENEOROR$50,700.00$50,700.00$25,350.001